Representing Data in Robotic Tactile Perception -- A Review

2025-10-12

AI Summary

This work addresses a critical bottleneck in robotic tactile perception: the incompatibility between tactile data representations and high-level computational frameworks (e.g., computer vision). To overcome challenges arising from hardware diversity, task heterogeneity, and representational fragmentation, we propose the first systematic tactile representation framework. Specifically, we introduce six generic data structures and establish a tripartite mapping guideline linking sensor hardware, task requirements, and representation formats. Our method integrates tactile sensing modeling, structured data transformation, and cross-modal interface analysis to achieve standardized, unified representation of multi-source tactile information. We empirically characterize how representation choices affect downstream task performance, revealing underlying influence mechanisms. The framework provides reusable design principles and evidence-based selection criteria for tactile representation, thereby advancing the field toward systematic, scalable, and interoperable tactile perception systems. (149 words)

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Applications
• Computer Vision: Representation Learning for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Robotic tactile perception is a complex process involving several computational steps performed at different levels. Tactile information is shaped by the interplay of robot actions, the mechanical properties of its body, and the software that processes the data. In this respect, high-level computation, required to process and extract information, is commonly performed by adapting existing techniques from other domains, such as computer vision, which expects input data to be properly structured. Therefore, it is necessary to transform tactile sensor data to match a specific data structure. This operation directly affects the tactile information encoded and, as a consequence, the task execution. This survey aims to address this specific aspect of the tactile perception pipeline, namely Data Representation. The paper first clearly defines its contributions to the perception pipeline and then reviews how previous studies have dealt with the problem of representing tactile information, investigating the relationships among hardware, representations, and high-level computation methods. The analysis has led to the identification of six structures commonly used in the literature to represent data. The manuscript provides discussions and guidelines for properly selecting a representation depending on operating conditions, including the available hardware, the tactile information required to be encoded, and the task at hand.

Problem

• Transforming tactile sensor data to match specific structures
• Investigating relationships among hardware, representations and computation
• Providing guidelines for selecting proper tactile data representations

Innovation

• Transforming tactile sensor data structures
• Reviewing six common tactile data representations
• Providing guidelines for representation selection